Export data from tables into partitioned folders on an external data lake

ABSTRACT

A database export system exports data using a plurality of nodes that process the data to generate structured result files that are partitioned by an export parameter in an export request. The database export system distributes the data and merges the files to avoid small file creation and increase processing speed via parallelism. The database export system generates the result files of a specified maximum size in a final format, where the files are processed merged in a temporary file format. The parallel processing is optimized and constrained per the amount of processing nodes, available memory, requested final file sizes, and operation based ordering to complete data exports in a scalable multi-stage approach.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a Continuation of U.S. patent application Ser. No.17/086,215 filed Oct. 30, 2020, which claims the benefit of priority toU.S. Provisional Patent Application Ser. No. 63/092,347, filed on Oct.15, 2020, the contents of which are incorporated herein in theirentireties.

TECHNICAL FIELD

The present disclosure generally relates to special-purpose machinesthat manage database data and improvements to such variants, and to thetechnologies by which such special-purpose machines become improvedcompared to other special-purpose machines for transmitting databasedata between databases connected by a network.

BACKGROUND

Databases are used for data storage and access in computingapplications. A goal of database storage is to provide enormous sums ofinformation in an organized manner so that it can be accessed, managed,and updated. In a database, data may be organized into rows, columns,and tables. Database data can be exported to a remote location (e.g.,external data lake) using a copy or export command. The data can beexported to a single location and additional tools can then be used tofurther partition the data into different folders at the remotelocation. The process of further partitioning the data into thedifferent folders creates unwanted time and computational overhead.

BRIEF DESCRIPTION OF THE DRAWINGS

Various ones of the appended drawings merely illustrate exampleembodiments of the present disclosure and should not be considered aslimiting its scope.

FIG. 1 is a block diagram illustrating an example computing environmentin which a network-based data warehouse system can implement multi-stageexport, according to some example embodiments.

FIG. 2 is a block diagram illustrating components of a compute servicemanager, according to some example embodiments.

FIG. 3 is a block diagram illustrating components of an executionplatform, according to some example embodiments.

FIG. 4 is a multistage export database architecture, according to someexample embodiments.

FIGS. 5-7 show example flow diagrams for performing multistage exports,according to some example embodiments.

FIG. 8 illustrates a diagrammatic representation of a machine in theform of a computer system within which a set of instructions may beexecuted for causing the machine to perform any one or more of themethodologies discussed herein, in accordance with some embodiments ofthe present disclosure.

DETAILED DESCRIPTION

The description that follows includes systems, methods, techniques,instruction sequences, and computing machine program products thatembody illustrative embodiments of the disclosure. In the followingdescription, for the purposes of explanation, numerous specific detailsare set forth in order to provide an understanding of variousembodiments of the inventive subject matter. It will be evident,however, to those skilled in the art, that embodiments of the inventivesubject matter may be practiced without these specific details. Ingeneral, well-known instruction instances, protocols, structures, andtechniques are not necessarily shown in detail.

As discussed, exporting data to a remote location in a structured format(e.g., multiple folders/partitions) can create unwanted overhead. Tothis end, a multi-stage database partition unloader can export data to aremote datastore such as a data lake, using a single database copycommand that uses a partition expression (e.g., “partition by”) toparallelize processing of the files into a plurality of result files atthe remote location using a plurality of nodes in the database in adistributed and scalable approach.

FIG. 1 illustrates an example shared data processing platform 100 inwhich a network-based data warehouse system 102 implements databasestream tracking (e.g., view streams), in accordance with someembodiments of the present disclosure. To avoid obscuring the inventivesubject matter with unnecessary detail, various functional componentsthat are not germane to conveying an understanding of the inventivesubject matter have been omitted from the figures. However, a skilledartisan will readily recognize that various additional functionalcomponents may be included as part of the shared data processingplatform 100 to facilitate additional functionality that is notspecifically described herein.

As shown, the shared data processing platform 100 comprises thenetwork-based data warehouse system 102, a cloud computing storageplatform 104 (e.g., a storage platform, an AWS® service such as S3,Microsoft Azure®, or Google Cloud Services®), and a remote computingdevice 106. The network-based data warehouse system 102 is anetwork-based system used for storing and accessing data (e.g.,internally storing data, accessing external remotely located data) in anintegrated manner, and reporting and analysis of the integrated datafrom the one or more disparate sources (e.g., the cloud computingstorage platform 104). The cloud computing storage platform 104comprises a plurality of computing machines and provides on-demandcomputer system resources such as data storage and computing power tothe network-based data warehouse system 102.

The remote computing device 106 (e.g., a user device such as a laptopcomputer) comprises one or more computing machines (e.g., a user devicesuch as a laptop computer) that execute a remote software component 108(e.g., browser accessed cloud service) to provide additionalfunctionality to users of the network-based data warehouse system 102.The remote software component 108 comprises a set of machine-readableinstructions (e.g., code) that, when executed by the remote computingdevice 106, cause the remote computing device 106 to provide certainfunctionality. The remote software component 108 may operate on inputdata and generates result data based on processing, analyzing, orotherwise transforming the input data. As an example, the remotesoftware component 108 can be a data provider or data consumer thatenables database tracking procedures, such as streams on shared tablesand views, as discussed in further detail below.

The network-based data warehouse system 102 comprises an accessmanagement system 110, a compute service manager 112, an executionplatform 114, and a database 116. The access management system 110enables administrative users to manage access to resources and servicesprovided by the network-based data warehouse system 102. Administrativeusers can create and manage users, roles, and groups, and usepermissions to allow or deny access to resources and services. Theaccess management system 110 can store shared data that securely managesshared access to the storage resources of the cloud computing storageplatform 104 amongst different users of the network-based data warehousesystem 102, as discussed in further detail below.

The compute service manager 112 coordinates and manages operations ofthe network-based data warehouse system 102. The compute service manager112 also performs query optimization and compilation as well as managingclusters of computing services that provide compute resources (e.g.,virtual warehouses, virtual machines, EC2 clusters). The compute servicemanager 112 can support any number of client accounts such as end usersproviding data storage and retrieval requests, system administratorsmanaging the systems and methods described herein, and othercomponents/devices that interact with compute service manager 112.

The compute service manager 112 is also coupled to database 116, whichis associated with the entirety of data stored on the shared dataprocessing platform 100. The database 116 stores data pertaining tovarious functions and aspects associated with the network-based datawarehouse system 102 and its users. For example, data to be tracked viastreams can be stored and accessed on the cloud computing storageplatform 104 (e.g., on S3) or stored and accessed on the database 116that is local to the network-based data warehouse system 102, accordingto some example embodiments.

In some embodiments, database 116 includes a summary of data stored inremote data storage systems as well as data available from one or morelocal caches. Additionally, database 116 may include informationregarding how data is organized in the remote data storage systems andthe local caches. Database 116 allows systems and services to determinewhether a piece of data needs to be accessed without loading oraccessing the actual data from a storage device. The compute servicemanager 112 is further coupled to an execution platform 114, whichprovides multiple computing resources (e.g., virtual warehouses) thatexecute various data storage and data retrieval tasks, as discussed ingreater detail below. In some example embodiments, the database 116includes a staging area for temporary files to be merged (e.g., arrowfiles) that are generated by the plurality of nodes.

Execution platform 114 is coupled to multiple data storage devices 124-1to 124-n that are part of a cloud computing storage platform 104. Insome embodiments, data storage devices 124-1 to 124-n are cloud-basedstorage devices located in one or more geographic locations. Forexample, data storage devices 124-1 to 124-n may be part of a publiccloud infrastructure or a private cloud infrastructure. Data storagedevices 124-1 to 124-n may be hard disk drives (HDDs), solid statedrives (SSDs), storage clusters, network storage systems or any otherdata storage technology (e.g., Amazon S3, Google Cloud Services, AzureData Lake, etc.). Additionally, cloud computing storage platform 104 mayinclude distributed file systems (such as Hadoop Distributed FileSystems (HDFS)), object storage systems, and the like.

The execution platform 114 comprises a plurality of compute nodes (e.g.,virtual warehouses). A set of processes on a compute node executes aquery plan compiled by the compute service manager 112. The set ofprocesses can include: a first process to execute the query plan; asecond process to monitor and delete micro-partition files using a leastrecently used (LRU) policy, and implement an out of memory (OOM) errormitigation process; a third process that extracts health informationfrom process logs and status information to send back to the computeservice manager 112; a fourth process to establish communication withthe compute service manager 112 after a system boot; and a fifth processto handle all communication with a compute cluster for a given jobprovided by the compute service manager 112 and to communicateinformation back to the compute service manager 112 and other computenodes of the execution platform 114.

The cloud computing storage platform 104 also comprises an accessmanagement system 118 and a web proxy 120. As with the access managementsystem 110, the access management system 118 allows users to create andmanage users, roles, and groups, and use permissions to allow or denyaccess to cloud services and resources. The access management system 110of the network-based data warehouse system 102 and the access managementsystem 118 of the cloud computing storage platform 104 can communicateand share information so as to enable access and management of resourcesand services shared by users of both the network-based data warehousesystem 102 and the cloud computing storage platform 104. The web proxy120 handles tasks involved in accepting and processing concurrent APIcalls, including traffic management, authorization and access control,monitoring, and API version management. The web proxy 120 provides HTTPproxy service for creating, publishing, maintaining, securing, andmonitoring APIs (e.g., REST APIs).

In some embodiments, communication links between elements of the shareddata processing platform 100 are implemented via one or more datacommunication networks. These data communication networks may utilizeany communication protocol and any type of communication medium. In someembodiments, the data communication networks are a combination of two ormore data communication networks (or sub-networks) coupled to oneanother. In alternative embodiments, these communication links areimplemented using any type of communication medium and any communicationprotocol.

As shown in FIG. 1, data storage devices 124-1 to 124-N are decoupledfrom the computing resources associated with the execution platform 114.That is, new virtual warehouses can be created and terminated in theexecution platform 114 and additional data storage devices can becreated and terminated on the cloud computing storage platform 104 in anindependent manner. This architecture supports dynamic changes to thenetwork-based data warehouse system 102 based on the changing datastorage/retrieval needs as well as the changing needs of the users andsystems accessing the shared data processing platform 100. The supportof dynamic changes allows network-based data warehouse system 102 toscale quickly in response to changing demands on the systems andcomponents within network-based data warehouse system 102. Thedecoupling of the computing resources from the data storage devices 124supports the storage of large amounts of data without requiring acorresponding large amount of computing resources. Similarly, thisdecoupling of resources supports a significant increase in the computingresources utilized at a particular time without requiring acorresponding increase in the available data storage resources.Additionally, the decoupling of resources enables different accounts tohandle creating additional compute resources to process data shared byother users without affecting the other users' systems. For instance, adata provider may have three compute resources and share data with adata consumer, and the data consumer may generate new compute resourcesto execute queries against the shared data, where the new computeresources are managed by the data consumer and do not affect or interactwith the compute resources of the data provider.

Compute service manager 112, database 116, execution platform 114, cloudcomputing storage platform 104, and remote computing device 106 areshown in FIG. 1 as individual components. However, each of computeservice manager 112, database 116, execution platform 114, cloudcomputing storage platform 104, and remote computing device 106 may beimplemented as a distributed system (e.g., distributed across multiplesystems/platforms at multiple geographic locations) connected by APIsand access information (e.g., tokens, login data). Additionally, each ofcompute service manager 112, database 116, execution platform 114, andcloud computing storage platform 104 can be scaled up or down(independently of one another) depending on changes to the requestsreceived and the changing needs of shared data processing platform 100.Thus, in the described embodiments, the network-based data warehousesystem 102 is dynamic and supports regular changes to meet the currentdata processing needs.

During typical operation, the network-based data warehouse system 102processes multiple jobs (e.g., queries) determined by the computeservice manager 112. These jobs are scheduled and managed by the computeservice manager 112 to determine when and how to execute the job. Forexample, the compute service manager 112 may divide the job intomultiple discrete tasks and may determine what data is needed to executeeach of the multiple discrete tasks. The compute service manager 112 mayassign each of the multiple discrete tasks to one or more nodes of theexecution platform 114 to process the task. The compute service manager112 may determine what data is needed to process a task and furtherdetermine which nodes within the execution platform 114 are best suitedto process the task. Some nodes may have already cached the data neededto process the task (due to the nodes having recently downloaded thedata from the cloud computing storage platform 104 for a previous job)and, therefore, may be a good candidate for processing the task.Metadata stored in the database 116 assists the compute service manager112 in determining which nodes in the execution platform 114 havealready cached at least a portion of the data needed to process thetask. One or more nodes in the execution platform 114 process the taskusing data cached by the nodes and data retrieved from the cloudcomputing storage platform 104. It is desirable to retrieve as much dataas possible from caches within the execution platform 114 because theretrieval speed is typically much faster than retrieving data from thecloud computing storage platform 104.

As shown in FIG. 1, the shared data processing platform 100 separatesthe execution platform 114 from the cloud computing storage platform104. In this arrangement, the processing resources and cache resourcesin the execution platform 114 operate independently of the data storagedevices 124-1 to 124-n in the cloud computing storage platform 104.Thus, the computing resources and cache resources are not restricted tospecific data storage devices 124-1 to 124-n. Instead, all computingresources and all cache resources may retrieve data from, and store datato, any of the data storage resources in the cloud computing storageplatform 104.

FIG. 2 is a block diagram illustrating components of the compute servicemanager 112, in accordance with some embodiments of the presentdisclosure. As shown in FIG. 2, a request processing service 202 managesreceived data storage requests and data retrieval requests (e.g., jobsto be performed on database data). For example, the request processingservice 202 may determine the data to process a received query (e.g., adata storage request or data retrieval request). The data may be storedin a cache within the execution platform 114 or in a data storage devicein cloud computing storage platform 104. A management console service204 supports access to various systems and processes by administratorsand other system managers. Additionally, the management console service204 may receive a request to execute a job and monitor the workload onthe system. The multistage export system 225 manages transmission ofdatabase data, such as exporting data to an external data store (e.g.,data lake) in a multi-stage approach, as discussed in further detailbelow.

The compute service manager 112 also includes a job compiler 206, a joboptimizer 208, and a job executor 210. The job compiler 206 parses a jobinto multiple discrete tasks and generates the execution code for eachof the multiple discrete tasks. The job optimizer 208 determines thebest method to execute the multiple discrete tasks based on the datathat needs to be processed. The job optimizer 208 also handles variousdata pruning operations and other data optimization techniques toimprove the speed and efficiency of executing the job. The job executor210 executes the execution code for jobs received from a queue ordetermined by the compute service manager 112.

A job scheduler and coordinator 212 sends received jobs to theappropriate services or systems for compilation, optimization, anddispatch to the execution platform 114. For example, jobs may beprioritized and processed in that prioritized order. In an embodiment,the job scheduler and coordinator 212 determines a priority for internaljobs that are scheduled by the compute service manager 112 with other“outside” jobs such as user queries that may be scheduled by othersystems in the database but may utilize the same processing resources inthe execution platform 114. In some embodiments, the job scheduler andcoordinator 212 identifies or assigns particular nodes in the executionplatform 114 to process particular tasks. A virtual warehouse manager214 manages the operation of multiple virtual warehouses implemented inthe execution platform 114. As discussed below, each virtual warehouseincludes multiple execution nodes that each include a cache and aprocessor (e.g., a virtual machine, an operating system level containerexecution environment).

Additionally, the compute service manager 112 includes a configurationand metadata manager 216, which manages the information related to thedata stored in the remote data storage devices and in the local caches(i.e., the caches in execution platform 114). The configuration andmetadata manager 216 uses the metadata to determine which datamicro-partitions need to be accessed to retrieve data for processing aparticular task or job. A monitor and workload analyzer 218 overseesprocesses performed by the compute service manager 112 and manages thedistribution of tasks (e.g., workload) across the virtual warehouses andexecution nodes in the execution platform 114. The monitor and workloadanalyzer 218 also redistributes tasks, as needed, based on changingworkloads throughout the network-based data warehouse system 102 and mayfurther redistribute tasks based on a user (e.g., “external”) queryworkload that may also be processed by the execution platform 114. Theconfiguration and metadata manager 216 and the monitor and workloadanalyzer 218 are coupled to a data storage device 220. The data storagedevice 220 in FIG. 2 represents any data storage device within thenetwork-based data warehouse system 102. For example, data storagedevice 220 may represent caches in execution platform 114, storagedevices in cloud computing storage platform 104, or any other storagedevice.

FIG. 3 is a block diagram illustrating components of the executionplatform 114, in accordance with some embodiments of the presentdisclosure. As shown in FIG. 3, execution platform 114 includes multiplevirtual warehouses, which are elastic clusters of compute instances,such as virtual machines. In the example illustrated, the virtualwarehouses include virtual compute node 1, virtual compute node 2, andvirtual warehouse n. Each virtual warehouse (e.g., EC2 cluster) includesmultiple execution nodes (e.g., virtual machines) that each include adata cache and a processor. The virtual warehouses can execute multipletasks in parallel by using the multiple execution nodes. As discussedherein, execution platform 114 can add new virtual warehouses and dropexisting virtual warehouses in real time based on the current processingneeds of the systems and users. This flexibility allows the executionplatform 114 to quickly deploy large amounts of computing resources whenneeded without being forced to continue paying for those computingresources when they are no longer needed. All virtual warehouses canaccess data from any data storage device (e.g., any storage device incloud computing storage platform 104).

Although each virtual warehouse shown in FIG. 3 includes three executionnodes, a particular virtual warehouse may include any number ofexecution nodes. Further, the number of execution nodes in a virtualwarehouse is dynamic, such that new execution nodes are created whenadditional demand is present, and existing execution nodes are deletedwhen they are no longer necessary (e.g., upon a query or jobcompletion).

Each virtual warehouse is capable of accessing any of the data storagedevices 124-1 to 124-n shown in FIG. 1. Thus, the virtual warehouses arenot necessarily assigned to a specific data storage device 124-1 to124-n and, instead, can access data from any of the data storage devices124-1 to 124-n within the cloud computing storage platform 104.Similarly, each of the execution nodes shown in FIG. 3 can access datafrom any of the data storage devices 124-1 to 124-n. For instance, thestorage device 124-1 of a first user (e.g., provider account user) maybe shared with a worker node in a virtual warehouse of another user(e.g., consumer account user), such that the other user can create adatabase (e.g., read-only database) and use the data in storage device124-1 directly without needing to copy the data (e.g., copy it to a newdisk managed by the consumer account user). In some embodiments, aparticular virtual warehouse or a particular execution node may betemporarily assigned to a specific data storage device, but the virtualwarehouse or execution node may later access data from any other datastorage device.

In the example of FIG. 3, virtual compute node 1 includes threeexecution nodes 302-1, 302-2, and 302-n. Execution node 302-1 includes acache 304-1 and a processor 306-1. Execution node 302-2 includes a cache304-2 and a processor 306-2. Execution node 302-n includes a cache 304-nand a processor 306-n. Each execution node 302-1, 302-2, and 302-n isassociated with processing one or more data storage and/or dataretrieval tasks. For example, a virtual warehouse may handle datastorage and data retrieval tasks associated with an internal service,such as a clustering service, a materialized view refresh service, afile compaction service, a storage procedure service, or a file upgradeservice. In other implementations, a particular virtual warehouse mayhandle data storage and data retrieval tasks associated with aparticular data storage system or a particular category of data.

Similar to virtual compute node 1 discussed above, virtual compute node2 includes three execution nodes 312-1, 312-2, and 312-n. Execution node312-1 includes a cache 314-1 and a processor 316-1. Execution node 312-2includes a cache 314-2 and a processor 316-2. Execution node 312-nincludes a cache 314-n and a processor 316-n. Additionally, virtualwarehouse 3 includes three execution nodes 322-1, 322-2, and 322-n.Execution node 322-1 includes a cache 324-1 and a processor 326-1.Execution node 322-2 includes a cache 324-2 and a processor 326-2.Execution node 322-n includes a cache 324-n and a processor 326-n.

In some embodiments, the execution nodes shown in FIG. 3 are statelesswith respect to the data the execution nodes are caching. For example,these execution nodes do not store or otherwise maintain stateinformation about the execution node, or the data being cached by aparticular execution node. Thus, in the event of an execution nodefailure, the failed node can be transparently replaced by another node.Since there is no state information associated with the failed executionnode, the new (replacement) execution node can easily replace the failednode without concern for recreating a particular state.

Although the execution nodes shown in FIG. 3 each include one data cacheand one processor, alternate embodiments may include execution nodescontaining any number of processors and any number of caches.Additionally, the caches may vary in size among the different executionnodes. The caches shown in FIG. 3 store, in the local execution node(e.g., local disk), data that was retrieved from one or more datastorage devices in cloud computing storage platform 104 (e.g., S3objects recently accessed by the given node). In some exampleembodiments, the cache stores file headers and individual columns offiles as a query downloads only columns useful for that query.

To improve cache hits and avoid overlapping redundant data stored in thenode caches, the job optimizer 208 assigns input file sets to the nodesusing a consistent hashing scheme to hash over table file names of thedata accessed (e.g., data in database 116 or database 122). Subsequentor concurrent queries accessing the same table file will therefore beperformed on the same node, according to some example embodiments.

As discussed, the nodes and virtual warehouses may change dynamically inresponse to environmental conditions (e.g., disaster scenarios),hardware/software issues (e.g., malfunctions), or administrative changes(e.g., changing from a large cluster to smaller cluster to lower costs).In some example embodiments, when the set of nodes changes, no data isreshuffled immediately. Instead, the least recently used replacementpolicy is implemented to eventually replace the lost cache contents overmultiple jobs. Thus, the caches reduce or eliminate the bottleneckproblems occurring in platforms that consistently retrieve data fromremote storage systems. Instead of repeatedly accessing data from theremote storage devices, the systems and methods described herein accessdata from the caches in the execution nodes, which is significantlyfaster and avoids the bottleneck problem discussed above. In someembodiments, the caches are implemented using high-speed memory devicesthat provide fast access to the cached data. Each cache can store datafrom any of the storage devices in the cloud computing storage platform104.

Further, the cache resources and computing resources may vary betweendifferent execution nodes. For example, one execution node may containsignificant computing resources and minimal cache resources, making theexecution node useful for tasks that make use of significant computingresources. Another execution node may contain significant cacheresources and minimal computing resources, making this execution nodeuseful for tasks that may use caching of large amounts of data. Yetanother execution node may contain cache resources providing fasterinput-output operations, useful for tasks that make use of fast scanningof large amounts of data. In some embodiments, the execution platform114 implements skew handling to distribute work amongst the cacheresources and computing resources associated with a particularexecution, where the distribution may be further based on the expectedtasks to be performed by the execution nodes. For example, an executionnode may be assigned more processing resources if the tasks performed bythe execution node become more processor-intensive. Similarly, anexecution node may be assigned more cache resources if the tasksperformed by the execution node may use a larger cache capacity.Further, some nodes may be executing much slower than others due tovarious issues (e.g., virtualization issues, network overhead). In someexample embodiments, the imbalances are addressed at the scan levelusing a file stealing scheme. In particular, whenever a node processcompletes scanning its set of input files, it requests additional filesfrom other nodes. If the one of the other nodes receives such a request,the node analyzes its own set (e.g., how many files are left in theinput file set when the request is received), and then transfersownership of one or more of the remaining files for the duration of thecurrent job (e.g., query). The requesting node (e.g., the file stealingnode) then receives the data (e.g., header data) and downloads the filesfrom the cloud computing storage platform 104 (e.g., from data storagedevice 124-1), and does not download the files from the transferringnode. In this way, lagging nodes can transfer files via file stealing ina way that does not worsen the load on the lagging nodes.

Although virtual warehouses 1, 2, and n are associated with the sameexecution platform 114, the virtual warehouses may be implemented usingmultiple computing systems at multiple geographic locations. Forexample, virtual compute node 1 can be implemented by a computing systemat a first geographic location, while virtual warehouses 2 and n areimplemented by another computing system at a second geographic location.In some embodiments, these different computing systems are cloud-basedcomputing systems maintained by one or more different entities.

Additionally, each virtual warehouse is shown in FIG. 3 as havingmultiple execution nodes. The multiple execution nodes associated witheach virtual warehouse may be implemented using multiple computingsystems at multiple geographic locations. For example, an instance ofvirtual compute node 1 implements execution nodes 302-1 and 302-2 on onecomputing platform at a geographic location and implements executionnode 302-n at a different computing platform at another geographiclocation. Selecting particular computing systems to implement anexecution node may depend on various factors, such as the level ofresources needed for a particular execution node (e.g., processingresource requirements and cache requirements), the resources availableat particular computing systems, communication capabilities of networkswithin a geographic location or between geographic locations, and whichcomputing systems are already implementing other execution nodes in thevirtual warehouse.

Execution platform 114 is also fault tolerant. For example, if onevirtual warehouse fails, that virtual warehouse is quickly replaced witha different virtual warehouse at a different geographic location.

A particular execution platform 114 may include any number of virtualwarehouses. Additionally, the number of virtual warehouses in aparticular execution platform is dynamic, such that new virtualwarehouses are created when additional processing and/or cachingresources are needed. Similarly, existing virtual warehouses may bedeleted when the resources associated with the virtual warehouse are nolonger necessary.

In some embodiments, the virtual warehouses may operate on the same datain cloud computing storage platform 104, but each virtual warehouse hasits own execution nodes with independent processing and cachingresources. This configuration allows requests on different virtualwarehouses to be processed independently and with no interferencebetween the requests. This independent processing, combined with theability to dynamically add and remove virtual warehouses, supports theaddition of new processing capacity for new users without impacting theperformance observed by the existing users.

FIG. 4 shows a multi-stage database partition unloader architecture 400,according to some example embodiments. In the example, a database userusing remote computing device 106 issues a single copy unload command tothe database 116 to export data to an external data store (e.g.,external data lake), where the copy unload command includes apartition-by parameter that specifies how the data should be processed(e.g., exported, merged, and distributed) to efficiently export the datato different partitions in the data lake.

As an example, if the data managed by the database are website events(e.g., click-stream logs) of users hitting the website(s) with eachrecorded by timestamp, the user may seek to export the data to anexternal datastore as files, but not in a single folder. Rather, theuser seeks to partition them by time, so that the user can analyze thedata in a structured approach (e.g., the user request, from the files,all click-stream logs of users that visited a certain month, or set ofmonths in a given year, etc.). As another example, a user can seek toexport data to different partitions in the external datastore, where thepartitions are organized by state information, country information, zipcode information, thereby enabling the user to only look for data abouta given zip-code by navigating to the correct folder, instead ofscanning all the data in a single folder.

Conventionally, if the user wants to export data into 2,000 folders(e.g., timestamp based partitions, etc.) in the external data lake theuser can issue 2,000 copy unload commands, where each one specifies oneof folders into which a portion of the data is unloaded, however thistype of approach is not efficient. Alternatively, in anotherconventional approach, the user can export the data into a single folderin the external data store and then use an external tool (e.g., ApacheSpark) to separate the data after export, however the external tools canrequire additional time and resources to configure, which is notpreferable for users.

One issue encountered by distributing the data to nodes for processingis the generation of small files, which can skew the processing andoverload certain nodes while other nodes may not have any data toprocess (due to quickly processing the small amount of data to which thenode is assigned). Further, if the generated export files are small,then later downstream applications will incur a loss of performance inhandling the small files. At a high level, architecture 400 solves theseexport and small file skew issues by distributing the files to eachnode, processing them using a lower projection operation into a firsttemporary format which can be easily serialized and deserialized (e.g.,Arrow file format), storing the temporary files locally for hashing andredistribution to then nodes, and then generating result files using anupper projection according to the user's request in a second finalformat, a non-Arrow format, such as Parquet, of a given size (e.g., 500MB), thereby cleaning up the small files and avoiding downstream smallfile performance issues.

With reference to FIG. 4, two worker nodes 405 and 450 are illustrated(e.g., node 302-1, node 302-2, FIG. 4), each having a plurality ofthreads 410A-410N and 455A-455N. Each of the nodes performs a tablescan, a projection per the export (e.g., lower projection to retrieverelevant data per the export request), and performs a partition unloadoperation to generate merge files 415. The merge files 415 are thenlocally hashed by the partition key from the export request to merge thefiles according to their destination partition (e.g., certain month, ifthe export command's partition request is timestamp based), whichgenerates merge files 420. The merge files 420 can then be stored byeach of the workers to an internal staging area that is internal to thedatabase, thereby avoiding egress charges and increasing access andlater processing operations. The merge files 420 then undergosubdividing hashing by the partition key to generate merge files 425,which funnels the files by a given partition towards other files thatbelong in the same given partition. As illustrated by the dashed linefor partition A and the dotted lines for partition B, the subdivide hashfurther merges and consolidates the files by partition thereby furtheravoiding small file processing. The merge files 425 are then hashed toredistribute them to the plurality of works as merge files 430 (e.g.,MergeExternalFiles_7, arrow files). In the example of FIG. 4 for clarityof the illustration, only a single worker node 405 is illustrated, butit is appreciated that the merge files 430 are likewise redistributed tothe other nodes using the hashing by the partition key. Each of themerge files 430 can further undergo an upper projection (e.g., toeliminate extra columns of output) to generate the final result files(e.g., result file 435, a 500 MB parquet file), which are then exportedto the external data lake in complete structured and partitioned formaccording to the single export request generated by the user.

FIG. 5 shows a flow diagram of an example method 500 for exporting datainto different folders using a multistage database export system,according to some example embodiments. At operation 505, the multistageexport system 225 receives an export command. The export command caninclude a partitioned-by parameter option to set how the files will bestructured at the destination (e.g., timestamp based, geographic databased, based on one or more specified columns or types of columns of thetables, etc.). Further, the export command can include the file type ofthe final files to be exported (e.g., Parquet, CSV, JSON) and the maxfile size per partition (e.g., 500 MB). An example of the command syntaxis included below, with explanatory comments denoted by forward slashes(“II”):

:::::::::::::::::::::CODE BEGIN:::::::::::::::::::::: COPY INTO {internalStage | externalStage | externalLocation } //specifies internalstage, external stage, and eternal location (data lake) FROM {[<namespace>.]<table_name> | ( <query> ) } //specifies file namestructure and query parameters to identify specific  data from tables tomultistage export [ PARTITION BY <partition_expression> ] //specifiesthe structure by which the result files are partitioned or  separated todifferent folders directories. E.g., by time, by  geographic location,by specific columns, etc.  <partition_expression> is an expression thatcan reference  columns (via aliases from the select list of the query).When  evaluated, it produces a string value - a prefix that is used to hold all files containing data belonging to the specified partition. [FILE_FORMAT = ( { FORMAT_NAME =  ‘[<namespace>.]<file_format_name>’ |TYPE = { CSV | JSON |  PARQUET } [ formatTypeOptions ] } ) //specifiesresult file type, and options such as Max file size:::::::::::::::::::::CODE END::::::::::::::::::::::

At operation 510, the plurality of nodes perform lower level projectionand unload, as discussed above with reference to FIG. 4. At operation515, the export data is merged at different levels of the exportarchitecture, as discussed in further detail below with reference toFIG. 6. At operation 520, the plurality of nodes perform high levelprojection operations after merging (e.g., merge files 430, discussedabove) to generate the result files as specified per the receivedcommand of operation 505 (e.g., in the specified format such as Parquet,each result file having a max size per the command).

FIG. 6 shows a flow diagram of a method 600 for performing hashes tocombine data in the export process, according to some exampleembodiments. At operation 605, local hashes are performed by each of theplurality of nodes. For example, with reference to worker node 405, eachof the threads of the worker node 405 (e.g., thread 410A, 410N) cangenerate merge files 415, which are merged internally across all threadsby locally hashing within the worker node 405 at operation 605. At thesame time (e.g., concurrently, in parallel), the other nodes (e.g., node450, etc.) can perform the local internal hashing in a similar manner atoperation 605, thereby generating merge file sets, each of which arehashed within a respective node to combine the data per the request(e.g., by partition key).

At operation 610, the multistage export system 225 performs sub-dividehashing across the datasets generated by each of the nodes. For example,the merge files 420 generated by each of the nodes is stored in aninternal staging datastore (e.g., database 116) and the multistageexport system 225 merges the data across the plurality of nodes tocombine data into similar partitions to generate merge files 430.

At operation 615, the multistage export system 225 performs furtherhashing to combine the data into similar partitions. For example, themultistage export system 225 hashes merge files 425 to create mergefiles 430 which are then distributed from the staging area to theplurality of nodes for upper projection processing to generate theresult files in the specified result file format (e.g., Parquet, JSON)and max file size (e.g., 500 MB).

FIG. 7 shows a flow diagram of a method 700 for efficiently generatingexport data for partitions using a multistage export and a plurality ofnodes, according to some example embodiments. Due to memory of the givennodes being of finite size, based on the size of the result filesrequested, the multistage export system 225 can adjust the operationalflow of the multistage architecture to avoid out-of-memory issues. Forexample, if a given export requests specifies 500 MB Parquet files(compressed), the data of each file uncompressed can be two to threegigabytes in size which can slow down a given node (e.g., a singleworker node producing 3 parquet files on 3 different threads may consumesignificant amounts of memory). To avoid these issues, the multistageexport system 225 can implement query plan export optimization of method700.

At operation 705, the available memory is identified. For example, thememory allocated to a given node is identified, where the availablememory for the node is split for use by the nodes' threads. At operation710, the requested result type and file size is identified.

At operation 715, the data processed by threads (e.g., threads410A-410N) is buffered using a partitioned overflow buffer (e.g., eachthread has a partitioned overflow buffer which includes N overflowbuffers). Rows will be distributed to individual Overflow Buffers byhashing on the Partition Key. Partitioned Overflow Buffer of a giventhread will track the number of partitions P that are present in theindividual Overflow Buffers, as well as the number of rows associatedwith each partition P. When an Overflow Buffer instance is full, themultistage export system 225 determines the number of partitions K to beprocessed from the Overflow Buffer. The system 225 flushes the K largestactive unloaders (based upon their estimated file size) and process theK partitions from the Overflow Buffer. If the Overflow Buffer containsmore partitions than can be concurrently supported, the multistageexport system 225 iterates for further processing.

Thread barrier processing of overflow buffers: In some exampleembodiments, once all rows have been received by the Partitioned Unloadprocess, each thread will have a Partitioned Overflow Buffer containingN Overflow Buffers to process. Rather than each thread separatelyprocessing their Partitioned Overflow Buffer and thus of their NOverflow Buffers, the multistage export system 225 instead adds abarrier stop and has each thread process the same Overflow Buffer fromall of the threads. Thus thread-J will process the Overflow Buffer[J]from all of the threads. Because the multistage export system 225distributed the rows into each Overflow Buffer based upon theirpartition key (e.g., the same partition files go into the same overflowbuffer for each thread), which allows the processing to aggregate thepartition data across all threads before unloading it to remote storage,thereby combining more data to create bigger result files and a smallernumber of overall files, due to the processing of the overflow bufferfor one through N at the same time. In other words, since one instanceis processing the same overflow buffer J from all the threads, theinstance effectively merges the partition-specific rows from across thethreads into files (e.g., arrow files) before it is uploaded. So if upontermination of all the threads' collective overflow buffers having only5 MB of data for a partition P, the instance only generates 1 arrow filefor partition P, rather than N (assuming all threads have at least 1 rowfor partition P) smaller arrow files. In this way, the number of filesfor output to network storage is reduced which reduces the networkoverhead and contention.

At operation 720, the multistage export system 225 sets a limit on thenumber of threads within a node that can implemented for exportprocessing based on the received query (e.g., available memory, nodequantity, and/or requested file sizes of the result files). For example,if there are 12 GB of memory in a node, which is to be divided acrossthe multiple threads, each is assigned 1.5 GB of memory total.Increasing the requested file in turn increases the row group size thatis inside of the file, which increases the amount of memory actuallyneeded to write the file. In the case where the required memory exceedsthe available memory (e.g., the parquet file size requested requires athread to use more than 1.5 GB of memory), then the multistage exportsystem 225 sets a limit to the active threads running simultaneously toensure the machine does not run out of memory. For example, if eightthreads are available, based on the available memory and the requestedfiles, the multistage export system 225 can set only four threads asactive to produce the parquet files, while keeping the other fourthreads of the node idle to avoid memory issues. In some exampleembodiments, in setting the active threads of a node, the number ofthreads is not changed but rather the number of threads that receivedata inputs for processing is changed, thereby changing the number ofthreads that have export processes to complete (e.g., while otherthreads that do not receive input data remain idle, with respect toexport processes per the export request).

At operation 725, the multistage export system 225 limits the operationsimplemented by threads and/or nodes based on the available memory andrequested result files. In some example embodiments, each thread insidea node runs independent and separate from the other nodes (e.g.,concurrently, in parallel). Thus, it can sometimes be the case thatduring a given multistage export operation, one thread is performing apartition unload operation and two other threads in the node areperforming merges. To avoid bottlenecks of memory, the multistage exportsystem 225 can limit, on an operation-type level, how many threads canperform a given operation. For example, and in accordance with someexample embodiments, the multistage export system 225 can implement thequery execution by the threads in a barrier stop manner, in which thethreads must complete a given set of operations (e.g., partitionunloads) before any one of the threads performs another type ofoperations (e.g., merge and generation of files). In some exampleembodiments, the operational limit can be modified further based on therequested file type. For example, if large parquet files are to begenerated, then the multistage export system 225 can set a limit thatall threads must complete projection and partition unloads before anyone of the nodes can generate a parquet file via merging. In contrast,if small result files are to be produced (e.g., small parquet files),the multistage export system 225 can set a limit that only a maximum oftwo threads can generate result files at any one time.

In some example embodiments, the threads are configured as task specificworker threads, e.g., unload nodes to perform partition unloadoperations, merge threads to perform merging operations, projectionthreads to perform projections, and so on. In those example embodiments,the barrier stop per operation approach can be implemented by notinputting data to nodes of a given type, thereby keeping the threads ofthat type idle. For example, at operation 725, the export system 225 canimplement the multiple threads such that only unload threads are activeand while any unload threads are active, no merge threads such beactive. To this end, the system 225 does not send any input to the mergethread, and thus the merge thread has no work to process and therebyremains idle (e.g., not consuming memory).

Further, in some example embodiments, all threads within node arededicated to a specific task (e.g., merging tasks) and the node isthereby a merge node, and other nodes can likewise be dedicated to othertasks (e.g., unload node with all unload threads). In some exampleembodiments, to conserve memory the system 225 can barrier stop theoperations at the node level, e.g., ensuring that only unload nodes areoperating, and no merge node is activated until all unload nodescomplete processes (e.g., by not sending data to a merge node).

FIG. 8 illustrates a diagrammatic representation of a machine 800 in theform of a computer system within which a set of instructions may beexecuted for causing the machine 800 to perform any one or more of themethodologies discussed herein, according to an example embodiment.Specifically, FIG. 8 shows a diagrammatic representation of the machine800 in the example form of a computer system, within which instructions816 (e.g., software, a program, an application, an applet, an app, orother executable code) for causing the machine 800 to perform any one ormore of the methodologies discussed herein may be executed. For example,the instructions 816 may cause the machine 800 to execute any one ormore operations of any one or more of the methods 500, 600, or 700. Asanother example, the instructions 816 may cause the machine 800 toimplement portions of the data flows illustrated in any one or more ofFIGS. 1-4. In this way, the instructions 816 transform a general,non-programmed machine into a particular machine 800 (e.g., the remotecomputing device 106, the access management system 110, the computeservice manager 112, the execution platform 114, the access managementsystem 118, the Web proxy 120, remote computing device 106) that isspecially configured to carry out any one of the described andillustrated functions in the manner described herein.

In alternative embodiments, the machine 800 operates as a standalonedevice or may be coupled (e.g., networked) to other machines. In anetworked deployment, the machine 800 may operate in the capacity of aserver machine or a client machine in a server-client networkenvironment, or as a peer machine in a peer-to-peer (or distributed)network environment. The machine 800 may comprise, but not be limitedto, a server computer, a client computer, a personal computer (PC), atablet computer, a laptop computer, a netbook, a smart phone, a mobiledevice, a network router, a network switch, a network bridge, or anymachine capable of executing the instructions 816, sequentially orotherwise, that specify actions to be taken by the machine 800. Further,while only a single machine 800 is illustrated, the term “machine” shallalso be taken to include a collection of machines 800 that individuallyor jointly execute the instructions 816 to perform any one or more ofthe methodologies discussed herein.

The machine 800 includes processors 810, memory 830, and input/output(I/O) components 850 configured to communicate with each other such asvia a bus 802. In an example embodiment, the processors 810 (e.g., acentral processing unit (CPU), a reduced instruction set computing(RISC) processor, a complex instruction set computing (CISC) processor,a graphics processing unit (GPU), a digital signal processor (DSP), anapplication-specific integrated circuit (ASIC), a radio-frequencyintegrated circuit (RFIC), another processor, or any suitablecombination thereof) may include, for example, a processor 812 and aprocessor 814 that may execute the instructions 816. The term“processor” is intended to include multi-core processors 810 that maycomprise two or more independent processors (sometimes referred to as“cores”) that may execute instructions 816 contemporaneously. AlthoughFIG. 8 shows multiple processors 810, the machine 800 may include asingle processor with a single core, a single processor with multiplecores (e.g., a multi-core processor), multiple processors with a singlecore, multiple processors with multiple cores, or any combinationthereof.

The memory 830 may include a main memory 832, a static memory 834, and astorage unit 836, all accessible to the processors 810 such as via thebus 802. The main memory 832, the static memory 834, and the storageunit 836 store the instructions 816 embodying any one or more of themethodologies or functions described herein. The instructions 816 mayalso reside, completely or partially, within the main memory 832, withinthe static memory 834, within the storage unit 836, within at least oneof the processors 810 (e.g., within the processor's cache memory), orany suitable combination thereof, during execution thereof by themachine 800.

The I/O components 850 include components to receive input, provideoutput, produce output, transmit information, exchange information,capture measurements, and so on. The specific I/O components 850 thatare included in a particular machine 800 will depend on the type ofmachine. For example, portable machines such as mobile phones willlikely include a touch input device or other such input mechanisms,while a headless server machine will likely not include such a touchinput device. It will be appreciated that the I/O components 850 mayinclude many other components that are not shown in FIG. 8. The I/Ocomponents 850 are grouped according to functionality merely forsimplifying the following discussion and the grouping is in no waylimiting. In various example embodiments, the I/O components 850 mayinclude output components 852 and input components 854. The outputcomponents 852 may include visual components (e.g., a display such as aplasma display panel (PDP), a light emitting diode (LED) display, aliquid crystal display (LCD), a projector, or a cathode ray tube (CRT)),acoustic components (e.g., speakers), other signal generators, and soforth. The input components 854 may include alphanumeric inputcomponents (e.g., a keyboard, a touch screen configured to receivealphanumeric input, a photo-optical keyboard, or other alphanumericinput components), point-based input components (e.g., a mouse, atouchpad, a trackball, a joystick, a motion sensor, or another pointinginstrument), tactile input components (e.g., a physical button, a touchscreen that provides location and/or force of touches or touch gestures,or other tactile input components), audio input components (e.g., amicrophone), and the like.

Communication may be implemented using a wide variety of technologies.The I/O components 850 may include communication components 864 operableto couple the machine 800 to a network 880 or devices 870 via a coupling882 and a coupling 872, respectively. For example, the communicationcomponents 864 may include a network interface component or anothersuitable device to interface with the network 880. In further examples,the communication components 864 may include wired communicationcomponents, wireless communication components, cellular communicationcomponents, and other communication components to provide communicationvia other modalities. The devices 870 may be another machine or any of awide variety of peripheral devices (e.g., a peripheral device coupledvia a universal serial bus (USB)). For example, as noted above, themachine 800 may correspond to any one of the remote computing device106, the access management system 110, the compute service manager 112,the execution platform 114, the access management system 118, the Webproxy 120, and the devices 870 may include any other of these systemsand devices.

The various memories (e.g., 830, 832, 834, and/or memory of theprocessor(s) 810 and/or the storage unit 836) may store one or more setsof instructions 816 and data structures (e.g., software) embodying orutilized by any one or more of the methodologies or functions describedherein. These instructions 816, when executed by the processor(s) 810,cause various operations to implement the disclosed embodiments.

As used herein, the terms “machine-storage medium,” “device-storagemedium,” and “computer-storage medium” mean the same thing and may beused interchangeably in this disclosure. The terms refer to a single ormultiple storage devices and/or media (e.g., a centralized ordistributed database, and/or associated caches and servers) that storeexecutable instructions and/or data. The terms shall accordingly betaken to include, but not be limited to, solid-state memories, andoptical and magnetic media, including memory internal or external toprocessors. Specific examples of machine-storage media, computer-storagemedia, and/or device-storage media include non-volatile memory,including by way of example semiconductor memory devices, e.g., erasableprogrammable read-only memory (EPROM), electrically erasableprogrammable read-only memory (EEPROM), field-programmable gate arrays(FPGAs), and flash memory devices; magnetic disks such as internal harddisks and removable disks; magneto-optical disks; and CD-ROM and DVD-ROMdisks. The terms “machine-storage media,” “computer-storage media,” and“device-storage media” specifically exclude carrier waves, modulateddata signals, and other such media, at least some of which are coveredunder the term “signal medium” discussed below.

In various example embodiments, one or more portions of the network 880may be an ad hoc network, an intranet, an extranet, a virtual privatenetwork (VPN), a local-area network (LAN), a wireless LAN (WLAN), awide-area network (WAN), a wireless WAN (WWAN), a metropolitan-areanetwork (MAN), the Internet, a portion of the Internet, a portion of thepublic switched telephone network (PSTN), a plain old telephone service(POTS) network, a cellular telephone network, a wireless network, aWi-Fi® network, another type of network, or a combination of two or moresuch networks. For example, the network 880 or a portion of the network880 may include a wireless or cellular network, and the coupling 882 maybe a Code Division Multiple Access (CDMA) connection, a Global Systemfor Mobile communications (GSM) connection, or another type of cellularor wireless coupling. In this example, the coupling 882 may implementany of a variety of types of data transfer technology, such as SingleCarrier Radio Transmission Technology (1×RTT), Evolution-Data Optimized(EVDO) technology, General Packet Radio Service (GPRS) technology,Enhanced Data rates for GSM Evolution (EDGE) technology, thirdGeneration Partnership Project (3GPP) including 3G, fourth generationwireless (4G) networks, Universal Mobile Telecommunications System(UMTS), High-Speed Packet Access (HSPA), Worldwide Interoperability forMicrowave Access (WiMAX), Long Term Evolution (LTE) standard, othersdefined by various standard-setting organizations, other long-rangeprotocols, or other data transfer technology.

The instructions 816 may be transmitted or received over the network 880using a transmission medium via a network interface device (e.g., anetwork interface component included in the communication components864) and utilizing any one of a number of well-known transfer protocols(e.g., hypertext transfer protocol (HTTP)). Similarly, the instructions816 may be transmitted or received using a transmission medium via thecoupling 872 (e.g., a peer-to-peer coupling) to the devices 870. Theterms “transmission medium” and “signal medium” mean the same thing andmay be used interchangeably in this disclosure. The terms “transmissionmedium” and “signal medium” shall be taken to include any intangiblemedium that is capable of storing, encoding, or carrying theinstructions 816 for execution by the machine 800, and include digitalor analog communications signals or other intangible media to facilitatecommunication of such software. Hence, the terms “transmission medium”and “signal medium” shall be taken to include any form of modulated datasignal, carrier wave, and so forth. The term “modulated data signal”means a signal that has one or more of its characteristics set orchanged in such a manner as to encode information in the signal.

The terms “machine-readable medium,” “computer-readable medium,” and“device-readable medium” mean the same thing and may be usedinterchangeably in this disclosure. The terms are defined to includeboth machine-storage media and transmission media. Thus, the termsinclude both storage devices/media and carrier waves/modulated datasignals.

The various operations of example methods described herein may beperformed, at least partially, by one or more processors that aretemporarily configured (e.g., by software) or permanently configured toperform the relevant operations. Similarly, the methods described hereinmay be at least partially processor-implemented. For example, at leastsome of the operations of the methods 500, 600, and 700 may be performedby one or more processors. The performance of certain of the operationsmay be distributed among the one or more processors, not only residingwithin a single machine, but also deployed across a number of machines.In some example embodiments, the processor or processors may be locatedin a single location (e.g., within a home environment, an officeenvironment, or a server farm), while in other embodiments theprocessors may be distributed across a number of locations.

The following numbered examples are embodiments:

Example 1. A method comprising: receiving, by one or more processors, anexport request to export data from tables of a database to an externaldatastore, the export request comprising a partition key forpartitioning the data across a plurality of partitions in the externaldatastore, portions of the data being distributed to each of a pluralityof nodes of the database for processing; generating, by the plurality ofnodes, first merge files by querying the tables, in parallel, accordingto the partition key, each node querying a corresponding portion of thedata using the partition key to generate one of the first merge files;generating second merge files by hashing the first merge files using thepartition key, the second merge files being merged according to thepartition key, portions of the second merge files stored across theplurality of nodes for processing; and generating, by the plurality ofnodes, result files in a format specified in the export request, eachnode querying a corresponding portion of merged data in the second mergefiles to generate one of the result files in the format specified in theexport request.

Example 2. The method of example 1, wherein the data is distributed toeach of the plurality of nodes for parallel processing, and wherein thefirst merge files are stored in a staging location in the database.

Example 3. The method of any of examples 1 or 2, wherein each nodeprocesses a corresponding portion of the data distributed to the nodeusing a plurality of threads.

Example 4. The method of any of examples 1-3, wherein each threadperforms a table scan on the data of the node followed by a projectionoperation and a partition unload operation to generate initial mergefiles.

Example 5. The method of any of examples 1-4, further comprising, foreach node, performing a local hash within each node to generate one ormore of the first merge files.

Example 6. The method of any of examples 1-5, wherein each node performsthe projection operation on a corresponding portion of the second mergefiles to generate one of the result files.

Example 7. The method of any of examples 1-6, wherein the export requestincludes a maximum file size for each of the result files to be createdat the external datastore.

Example 8. The method of any of examples 1-7, wherein some of theplurality of nodes generate small files that are combined by mergingusing hashing to generate result files of the maximum file size.

Example 9. The method of any of examples 1-8, wherein the first mergefiles are in a temporary serializable format for merging by hashing.

Example 10. The method of any of examples 1-9, wherein the temporaryserializable format is arrow file format, and wherein the format of theresult files is a non-arrow format.

Example 11. The method of any of examples 1-10, wherein the non-arrowformat is comma separated value (CSV) format.

Example 12. A system comprising: one or more processors of a machine;and a memory storing instructions that, when executed by the one or moreprocessors, cause the machine to perform operations comprising:receiving an export request to export data from tables of a database toan external datastore, the export request comprising a partition key forpartitioning the data across a plurality of partitions in the externaldatastore that is external to the database, portions of the data beingdistributed to each of a plurality of nodes of the database forprocessing; generating, by the plurality of nodes, first merge files byquerying the tables, in parallel, according to the partition key, eachnode querying a corresponding portion of the data using the partitionkey to generate one of the first merge files, the first merge filesstored in a staging location in the database; generating second mergefiles by hashing the first merge files using the partition key, thesecond merge files being merged according to the partition key, portionsof the second merge files stored across the plurality of nodes forprocessing; and generating, by the plurality of nodes, result files in aformat specified in the export request, each node querying acorresponding portion of merged data in the second merge files togenerate one of the result files in the format specified in the exportrequest.

Example 13. The system of example 12, wherein the data is distributed toeach of the plurality of nodes for parallel processing.

Example 14. The system of any of examples 12 or 13, wherein each nodeprocesses a corresponding portion of the data distributed to the nodeusing a plurality of threads.

Example 15. The system of any of examples 12-14, wherein each threadperforms a table scan on the data of the node followed by a projectionoperation and a partition unload operation to generate initial mergefiles.

Example 16. The system of any of examples 12-15, further comprising, foreach node, performing a local hash within each node to generate one ormore of the first merge files.

Example 17. The system of any of examples 12-16, wherein each nodeperforms the projection operation on a corresponding portion of thesecond merge files to generate one of the result files.

Example 18. The system of any of examples 12-17, wherein the exportrequest includes a maximum file size for each of the result files to becreated at the external datastore.

Example 19. The system of any of examples 12-18, wherein some of theplurality of nodes generate small files that are combined by mergingusing hashing to generate result files of the maximum file size.

Example 20. The system of any of examples 12-19, wherein the first mergefiles are in a temporary serializable format for merging by hashing.

Example 21. The system of any of examples 12-21, wherein the temporaryserializable format is arrow file format, and wherein the format of theresult files is a non-arrow format.

Example 22. The system of any of examples 12-21, wherein the non-arrowformat is comma separated value (CSV) format.

Example 23. A computer-storage medium embodying instructions that, whenexecuted by a machine, cause the machine to perform operationscomprising: receiving an export request to export data from tables of adatabase to an external datastore, the export request comprising apartition key for partitioning the data across a plurality of partitionsin the external datastore that is external to the database, portions ofthe data being distributed to each of a plurality of nodes of thedatabase for processing; generating, by the plurality of nodes, firstmerge files by querying the tables, in parallel, according to thepartition key, each node querying a corresponding portion of the datausing the partition key to generate one of the first merge files, thefirst merge files stored in a staging location in the database;generating second merge files by hashing the first merge files using thepartition key, the second merge files being merged according to thepartition key, portions of the second merge files stored across theplurality of nodes for processing; and generating, by the plurality ofnodes, result files in a format specified in the export request, eachnode querying a corresponding portion of merged data in the second mergefiles to generate one of the result files in the format specified in theexport request.

Example 24. The computer-storage medium of example 23, wherein the datais distributed to each of the plurality of nodes for parallelprocessing.

Example 25. The computer-storage medium of any of examples 23 or 24,wherein each node processes a corresponding portion of the datadistributed to the node using a plurality of threads.

Example 26. The computer-storage medium of any of examples 23-25,wherein each thread performs a table scan on the data of the nodefollowed by a projection operation and a partition unload operation togenerate initial merge files.

Example 27. The computer-storage medium of any of examples 23-26,further comprising, for each node, performing a local hash within eachnode to generate one or more of the first merge files.

Example 28. The computer-storage medium of any of examples 23-27,wherein each node performs the projection operation on a correspondingportion of the second merge files to generate one of the result files.

Example 29. The computer-storage medium of any of examples 23-28,wherein the export request includes a maximum file size for each of theresult files to be created at the external datastore.

Example 30. The computer-storage medium of any of examples 23-29,wherein some of the plurality of nodes generate small files that arecombined by merging using hashing to generate result files of themaximum file size.

Example 31. A method comprising: receiving, by one or more processors,an export request to export data from a database to an externaldatastore in result files having a file size limit, portions of the databeing distributed to each of a plurality of nodes of the database forprocessing by threads within each node; activating a subset of threadsfor one or more of the plurality of nodes to export the data based onthe file size limit specified in the export request; generating, by thesubset of threads within each node, first merge files by querying acorresponding portion of the data distributed to the node to generateone of the first merge files; generating, by the subset of threadswithin each node, second merge files by hashing the first merge filesusing a partition key specified in the export request; generating, bythe subset of threads within each node, result files by each nodequerying a corresponding portion of merged data in the second mergefiles to generate one of the result files; and storing the result filesin the external datastore.

Example 32. The method of example 31, wherein threads not included inthe subset of threads activated based on the file size limit are idleduring exporting of the data to the external datastore.

Example 33. The method of any of example 31 or 32, wherein only thesubset of the threads are activated based on a finite amount of memoryin each node, wherein each thread uses a portion of memory to performexport processing, and wherein a total quantity of portions of eachthread being activated to generate result files of the file size limitspecified in the export request exceeds the finite amount of memory inthe node.

Example 34. The method of any of examples 31-33, further comprising:receiving, by one or more processors, an additional export request toexport other data from the database to an additional external datastorein result file sizes having a smaller file size limit that is smallerthan the file size limit of the data exported to the external datastore.

Example 35. The method of any of examples 31-34, further comprising: inresponse to receiving the additional export request, activating a largersubset of threads for one or more of the plurality of nodes to exportthe other data based on the smaller file size limit specified in theadditional export request being smaller than the file size limit.

Example 36. The method of any of examples 31-35, wherein the largersubset of threads includes one or more threads not included in subset ofthreads activated in response to the file size limit in the exportrequest.

Example 37. The method of any of examples 31-36, wherein the data isdistributed to each of the plurality of nodes for parallel processing,and wherein the first merge files are stored in a staging location inthe database.

Example 38. The method of any of examples 31-37, wherein each thread inthe subset performs a table scan on the data of a corresponding nodefollowed by a projection operation and a partition unload operation togenerate initial merge files.

Example 39. The method of any of examples 31-38, further comprising, foreach node, performing a local hash within each node across the subset ofthreads to generate one or more of the first merge files.

Example 40. The method of any of examples 31-39, wherein each nodeperforms the projection operation on a corresponding portion of thesecond merge files to generate one of the result files.

Example 41. The method of any of examples 31-40, wherein some of theplurality of nodes generate, using the subset of threads, small filesthat are combined by merging using hashing to generate result files ofthe file size limit included in the export request.

Example 42. The method of any of examples 31-41, wherein the first mergefiles are in a temporary serializable format for merging by hashing.

Example 43. The method of any of examples 31-42, wherein the temporaryserializable format is arrow file format, and wherein a format of theresult files is a non-arrow format.

Example 44. A system comprising: one or more processors; and one or morememories storing instructions that, when executed by the one or moreprocessors, cause the one or more processors to perform operationscomprising: receiving an export request to export data from a databaseto an external datastore in result files having a file size limit,portions of the data being distributed to each of a plurality of nodesof the database for processing by threads within each node; activating asubset of threads for one or more of the plurality of nodes to exportthe data based on the file size limit specified in the export request;generating, by the subset of threads within each node, first merge filesby querying a corresponding portion of the data distributed to the nodeto generate one of the first merge files; generating, by the subset ofthreads within each node, second merge files by hashing the first mergefiles using a partition key specified in the export request; generating,by the subset of threads within each node, result files by each nodequerying a corresponding portion of merged data in the second mergefiles to generate one of the result files; and storing the result filesin the external datastore.

Example 45. The system of example 44, wherein threads not included inthe subset of threads activated based on the file size limit are idleduring exporting of the data to the external datastore, wherein only thesubset of the threads are activated based on a finite amount of memoryin each node, wherein each thread uses a portion of memory to performexport processing, and wherein a total quantity of portions of eachthread being activated to generate result files of the file size limitspecified in the export request exceeds the finite amount of memory inthe node.

Example 46. The system of any of examples 44 and 45, further comprising:receiving, by one or more processors, an additional export request toexport other data from the database to an additional external datastorein result file sizes having a smaller file size limit that is smallerthan the file size limit of the data exported to the external datastore.

Example 47. The system of any of examples 44-46, further comprising: inresponse to receiving the additional export request, activating a largersubset of threads for one or more of the plurality of nodes to exportthe other data based on the smaller file size limit specified in theadditional export request being smaller than the file size limit.

Example 48. The system of any of examples 44-47, wherein the largersubset of threads includes one or more threads not included in subset ofthreads activated in response to the file size limit in the exportrequest.

Example 49. The system of any of examples 44-48, wherein the data isdistributed to each of the plurality of nodes for parallel processing,and wherein the first merge files are stored in a staging location inthe database.

Example 50. The system of any of examples 44-49, wherein each thread inthe subset performs a table scan on the data of a corresponding nodefollowed by a projection operation and a partition unload operation togenerate initial merge files.

Example 51. The system of any of examples 44-50, further comprising, foreach node, performing a local hash within each node across the subset ofthreads to generate one or more of the first merge files.

Example 52. The system of any of examples 44-51, wherein each nodeperforms the projection operation on a corresponding portion of thesecond merge files to generate one of the result files.

Example 53. The system of any of examples 44-52, wherein some of theplurality of nodes generate, using the subset of threads, small filesthat are combined by merging using hashing to generate result files ofthe file size limit included in the export request.

Example 54. The system of any of examples 44-53, wherein the first mergefiles are in a temporary serializable format for merging by hashing.

Example 55. The system of any of examples 44-54, wherein the temporaryserializable format is arrow file format, and wherein a format of theresult files is a non-arrow format.

Example 56. A non-transitory computer-storage medium embodyinginstructions that, when executed by a machine, cause the machine toperform operations comprising: receiving an export request to exportdata from a database to an external datastore in result files having afile size limit, portions of the data being distributed to each of aplurality of nodes of the database for processing by threads within eachnode; activating a subset of threads for one or more of the plurality ofnodes to export the data based on the file size limit specified in theexport request; generating, by the subset of threads within each node,first merge files by querying a corresponding portion of the datadistributed to the node to generate one of the first merge files;generating, by the subset of threads within each node, second mergefiles by hashing the first merge files using a partition key specifiedin the export request; generating, by the subset of threads within eachnode, result files by each node querying a corresponding portion ofmerged data in the second merge files to generate one of the resultfiles; and storing the result files in the external datastore.

Example 57. The non-transitory computer-storage medium of example 56,wherein threads not included in the subset of threads activated based onthe file size limit are idle during exporting of the data to theexternal datastore, wherein only the subset of the threads are activatedbased on a finite amount of memory in each node, wherein each threaduses a portion of memory to perform export processing, and

wherein a total quantity of portions of each thread being activated togenerate result files of the file size limit specified in the exportrequest exceeds the finite amount of memory in the node.

Example 58. The non-transitory computer-storage medium of any ofexamples 56-57, further comprising: receiving, by one or moreprocessors, an additional export request to export other data from thedatabase to an additional external datastore in result file sizes havinga smaller file size limit that is smaller than the file size limit ofthe data exported to the external datastore.

Example 59. The non-transitory computer-storage medium of any ofexamples 56-58, further comprising: in response to receiving theadditional export request, activating a larger subset of threads for oneor more of the plurality of nodes to export the other data based on thesmaller file size limit specified in the additional export request beingsmaller than the file size limit.

Example 60. The non-transitory computer-storage medium of any ofexamples 56-59, wherein the larger subset of threads includes one ormore threads not included in subset of threads activated in response tothe file size limit in the export request.

Although the embodiments of the present disclosure have been describedwith reference to specific example embodiments, it will be evident thatvarious modifications and changes may be made to these embodimentswithout departing from the broader scope of the inventive subjectmatter. Accordingly, the specification and drawings are to be regardedin an illustrative rather than a restrictive sense. The accompanyingdrawings that form a part hereof show, by way of illustration, and notof limitation, specific embodiments in which the subject matter may bepracticed. The embodiments illustrated are described in sufficientdetail to enable those skilled in the art to practice the teachingsdisclosed herein. Other embodiments may be used and derived therefrom,such that structural and logical substitutions and changes may be madewithout departing from the scope of this disclosure. This DetailedDescription, therefore, is not to be taken in a limiting sense, and thescope of various embodiments is defined only by the appended claims,along with the full range of equivalents to which such claims areentitled.

Such embodiments of the inventive subject matter may be referred toherein, individually and/or collectively, by the term “invention” merelyfor convenience and without intending to voluntarily limit the scope ofthis application to any single invention or inventive concept if morethan one is in fact disclosed. Thus, although specific embodiments havebeen illustrated and described herein, it should be appreciated that anyarrangement calculated to achieve the same purpose may be substitutedfor the specific embodiments shown. This disclosure is intended to coverany and all adaptations or variations of various embodiments.Combinations of the above embodiments, and other embodiments notspecifically described herein, will be apparent, to those of skill inthe art, upon reviewing the above description.

In this document, the terms “a” or “an” are used, as is common in patentdocuments, to include one or more than one, independent of any otherinstances or usages of “at least one” or “one or more.” In thisdocument, the term “or” is used to refer to a nonexclusive or, such that“A or B” includes “A but not B,” “B but not A,” and “A and B,” unlessotherwise indicated. In the appended claims, the terms “including” and“in which” are used as the plain-English equivalents of the respectiveterms “comprising” and “wherein.” Also, in the following claims, theterms “including” and “comprising” are open-ended; that is, a system,device, article, or process that includes elements in addition to thoselisted after such a term in a claim is still deemed to fall within thescope of that claim.

1. A method comprising: receiving, by one or more processors, an exportrequest to export data from tables of a database to an externaldatastore, the export request comprising a partition key forpartitioning the data across a plurality of partitions in the externaldatastore and further comprising an export file size for each resultfile of the export data to be exported to the plurality of partitions inthe external datastore; generating, on a plurality of nodes, first mergefiles by querying the tables according to the partition key, the firstmerge files comprising smaller files that are smaller than the exportfile size, the smaller files being generated based on the partition keyand the export file size specified in the export request; storing thefirst merge files from the plurality of nodes on a datastore;generating, on the datastore, second merge files by hashing the firstmerge files using the partition key to merge the smaller files intolarger files of the second merge files; distributing the second mergefiles to the plurality of nodes for processing; and generating, on theplurality of nodes, result files in a format specified in the exportrequest, the result files being partitioned according to the partitionkey and sized according to the export file size specified in the exportrequest.
 2. The method of claim 1, wherein each node of the plurality ofnodes generates a portion of the first merge files using a plurality ofthreads, and wherein each thread performs a table scan on the exportdata processed by the thread.
 3. The method of claim 2, wherein eachthread performs a projection operation on the export data generated fromthe table scan and a partition unload operation to generate initialmerge files.
 4. The method of claim 3, wherein each node performs theprojection operation on a corresponding portion of the second mergefiles to generate one of the result files.
 5. The method of claim 1,wherein the first merge files are in a temporary serializable format formerging by hashing.
 6. The method of claim 5, wherein the temporaryserializable format is an arrow file format, and wherein the format ofthe result files is a non-arrow format.
 7. The method of claim 6,wherein the non-arrow format is a comma separated value format.
 8. Asystem comprising: one or more processors of a machine; and at least onememory storing instructions that, when executed by the one or moreprocessors, cause the machine to perform operations comprising:receiving an export request to export data from tables of a database toan external datastore, the export request comprising a partition key forpartitioning the data across a plurality of partitions in the externaldatastore and further comprising an export file size for each resultfile of the export data to be exported to the plurality of partitions inthe external datastore; generating, on a plurality of nodes, first mergefiles by querying the tables according to the partition key, the firstmerge files comprising smaller files that are smaller than the exportfile size, the smaller files being generated based on the partition keyand the export file size specified in the export request; storing thefirst merge files from the plurality of nodes on a datastore;generating, on the datastore, second merge files by hashing the firstmerge files using the partition key to merge the smaller files intolarger files of the second merge files; distributing the second mergefiles to the plurality of nodes for processing; and generating, on theplurality of nodes, result files in a format specified in the exportrequest, the result files being partitioned according to the partitionkey and sized according to the export file size specified in the exportrequest.
 9. The system of claim 8, wherein each node of the plurality ofnodes generates a portion of the first merge files using a plurality ofthreads, and wherein each thread performs a table scan on the exportdata processed by the thread.
 10. The system of claim 9, wherein eachthread performs a projection operation on the export data generated fromthe table scan and a partition unload operation to generate initialmerge files.
 11. The system of claim 10, wherein each node performs theprojection operation on a corresponding portion of the second mergefiles to generate one of the result files.
 12. The system of claim 8,wherein the first merge files are in a temporary serializable format formerging by hashing.
 13. The system of claim 12, wherein the temporaryserializable format is an arrow file format, and wherein the format ofthe result files is a non-arrow format.
 14. The system of claim 13,wherein the non-arrow format is a comma separated value format.
 15. Anon-transitory computer-storage medium embodying instructions that, whenexecuted by a machine, cause the machine to perform operationscomprising: receiving an export request to export data from tables of adatabase to an external datastore, the export request comprising apartition key for partitioning the data across a plurality of partitionsin the external datastore and further comprising an export file size foreach result files of the export data to be exported to the plurality ofpartitions in the external datastore; generating, on a plurality ofnodes, first merge files by querying the tables according to thepartition key, the first merge files comprising smaller files that aresmaller than the export file size, the smaller files being generatedbased on the partition key and the export file size specified in theexport request; storing the first merge files from the plurality ofnodes on a datastore; generating, on the datastore, second merge filesby hashing the first merge files using the partition key to merge thesmaller files into larger files of the second merge files; distributingthe second merge files to the plurality of nodes for processing; andgenerating, on the plurality of nodes, result files in a formatspecified in the export request, the result files being partitionedaccording to the partition key and sized according to the export filesize specified in the export request.
 16. The non-transitorycomputer-storage medium of claim 15, wherein each node of the pluralityof nodes generates a portion of the first merge files using a pluralityof threads, and wherein each thread performs a table scan on the exportdata processed by the thread.
 17. The non-transitory computer-storagemedium of claim 16, wherein each thread performs a projection operationon the export data generated from the table scan and a partition unloadoperation to generate initial merge files.
 18. The non-transitorycomputer-storage medium of claim 17, wherein each node performs theprojection operation on a corresponding portion of the second mergefiles to generate one of the result files.
 19. The non-transitorycomputer-storage medium of claim 15, wherein the first merge files arein a temporary serializable format for merging by hashing.
 20. Thenon-transitory computer-storage medium of claim 19, wherein thetemporary serializable format is an arrow file format, and wherein theformat of the result files is a non-arrow format.